Generalized Gaussian Process (GGP) model.
We next consider a model where latents are drawn from a Gaussian process (GP), which can be seen as a simplified GP state-space model [1]}, [2]}.
The goal here is a flexible, data-driven model for non-linear trends in \(f_{1:T}\)  without an explosion of parameters to learn.
Our model is again an instance of Eq. (REF ), with a GP for the prior over the latents:
\(f_{1:T} \sim \text{GP}( m_{\alpha }(t), k_{\alpha }(t,t^{\prime }) )\) . We assume a constant mean \(m_{\alpha }(t) = c\)  and squared exponential covariance kernel \(k_{\alpha }(t, t^{\prime }) = a^2 \exp \left(-\frac{(t - t^{\prime })^2}{2 \ell ^2}\right)\) .
The parameters \(\alpha = \lbrace c, a, \ell \rbrace \) , with \(a > 0\)  and \(\ell > 0\) , are given vague unimodal priors (see App. ).
